Jingwen Gao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jingwen Gao has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Tingting Liu
College of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao 266590, China
2 shared publications - Yantai Zhang
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
2 shared publications - Hao Wu
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
1 shared publication - Jun Ma
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
1 shared publication - Xiang Guo
School of Urban Construction, Changzhou University, Changzhou 213164, China
1 shared publication - Xiang Wang
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
1 shared publication
Latest Publications
- Open Access
Influence of Ground-Motion Intensity Measure Selection on Bayesian Fragility Analysis of RCS Frame Structures
This study focuses on RCS frame structures and selects six different types of ground-motion intensity measures (IMs), including peak ground acceleration (PGA), spectral acceleration at the fundamental period Sa(T1), the modified intensity measure S* considering period elongation effects, IM12 and IM123 accounting for higher-mode effects, and Housner intensity (HI). Based on a set of near-fault pulse-like ground-motion records, a Bayesian seismic fragility analysis characterized by different IMs is conducted. This study reveals the influence of these IMs on the estimation of fragility parameters under three limit states—immediate occupancy (IO), life safety (LS), and collapse prevention (CP)—using both uniform non-informative priors and lognormal weakly informative priors. The results indicate that, in terms of the applicability of IMs across different limit states, all IMs exhibit highly stable fragility parameters in the elastic IO stage, where the results from maximum likelihood estimation (MLE), uniform priors, and lognormal priors are nearly identical, suggesting that sufficient sample information renders the influence of priors negligible. In contrast, in the CP stage, characterized by strong nonlinearity and collapse, the differences among IMs become most pronounced. HI consistently yields stable results across all methods with almost no variation. When the structure enters the CP stage with small samples and strong nonlinearity, the lognormal prior effectively promotes distribution convergence, suppresses over-dispersion, and corrects asymmetry, significantly improving the robustness of parameter estimation. Notably, different IMs exhibit varying sensitivity to Bayesian priors, among which S* and HI are the least sensitive, demonstrating strong inherent stability and minimal dependence on prior constraints.
Open AccessInfluence of Ground-Motion Intensity Measure Selection on Bayesian Fragility Analysis of RCS Frame Structures
This study focuses on RCS frame structures and selects six different types of ground-motion intensity measures (IMs), including peak ground acceleration (PGA), spectral acceleration at the fundamental period Sa(T1), the modified intensity measure S* considering period elongation effects, IM12 and IM123 accounting for higher-mode effects, and Housner intensity (HI). Based on a set of near-fault pulse-like ground-motion records, a Bayesian seismic fragility analysis characterized by different IMs is conducted. This study reveals the influence of these IMs on the estimation of fragility parameters under three limit states—immediate occupancy (IO), life safety (LS), and collapse prevention (CP)—using both uniform non-informative priors and lognormal weakly informative priors. The results indicate that, in terms of the applicability of IMs across different limit states, all IMs exhibit highly stable fragility parameters in the elastic IO stage, where the results from maximum likelihood estimation (MLE), uniform priors, and lognormal priors are nearly identical, suggesting that sufficient sample information renders the influence of priors negligible. In contrast, in the CP stage, characterized by strong nonlinearity and collapse, the differences among IMs become most pronounced. HI consistently yields stable results across all methods with almost no variation. When the structure enters the CP stage with small samples and strong nonlinearity, the lognormal prior effectively promotes distribution convergence, suppresses over-dispersion, and corrects asymmetry, significantly improving the robustness of parameter estimation. Notably, different IMs exhibit varying sensitivity to Bayesian priors, among which S* and HI are the least sensitive, demonstrating strong inherent stability and minimal dependence on prior constraints.
- Open Access
Optimization of Sa(T1)-Based Combined Ground Motion Intensity Measure Using Simulated Annealing Algorithm in Seismic Fragility Analysis of RCS Frame Structures
This study presents a seismic fragility analysis of reinforced concrete column–steel beam (RCS) frame structures using an enhanced version of the Park–Ang damage model. The applicability of various seismic intensity measures (IMs) in fragility assessment was evaluated. Furthermore, a two-parameter IM was refined through simulated annealing optimization. Initially, the damage evolution of the structure under both near-field and far-field ground motions was investigated using a modified Park–Ang model tailored for RCS systems from the literature. Subsequently, seismic fragility was assessed through multiple stripe analysis, developing fragility curves for distinct damage limit states under the two ground motion types. The effectiveness of 22 different IMs was then examined across these limit states. A two-parameter IM that accounts for the softening period was identified as particularly effective in capturing ground motion uncertainty. This measure was further optimized by applying simulated annealing to minimize the record-to-record variability (βRTR), targeting its period coefficient (n) and weighting factor (α). Finally, the enhanced IM’s sufficiency and scaling robustness were validated. Results indicate that near-field ground motions induce considerably more severe damage in RCS frames compared to far-field motions, with damage concentrating in lower stories. The optimized IM achieved reductions in βRTR ranging from 8.7% to 38.1% across different damage states.
Open AccessOptimization of Sa(T1)-Based Combined Ground Motion Intensity Measure Using Simulated Annealing Algorithm in Seismic Fragility Analysis of RCS Frame Structures
This study presents a seismic fragility analysis of reinforced concrete column–steel beam (RCS) frame structures using an enhanced version of the Park–Ang damage model. The applicability of various seismic intensity measures (IMs) in fragility assessment was evaluated. Furthermore, a two-parameter IM was refined through simulated annealing optimization. Initially, the damage evolution of the structure under both near-field and far-field ground motions was investigated using a modified Park–Ang model tailored for RCS systems from the literature. Subsequently, seismic fragility was assessed through multiple stripe analysis, developing fragility curves for distinct damage limit states under the two ground motion types. The effectiveness of 22 different IMs was then examined across these limit states. A two-parameter IM that accounts for the softening period was identified as particularly effective in capturing ground motion uncertainty. This measure was further optimized by applying simulated annealing to minimize the record-to-record variability (βRTR), targeting its period coefficient (n) and weighting factor (α). Finally, the enhanced IM’s sufficiency and scaling robustness were validated. Results indicate that near-field ground motions induce considerably more severe damage in RCS frames compared to far-field motions, with damage concentrating in lower stories. The optimized IM achieved reductions in βRTR ranging from 8.7% to 38.1% across different damage states.